


'''simple docstring'''


import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCAmelCase_						:  Optional[Any]				  =						2





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   *,  # begin keyword-only arguments
							    __snake_case="<s>"       ,   __snake_case="<pad>"       ,   __snake_case="</s>"       ,   __snake_case="<unk>"       ,   __snake_case=None       ,   ):
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= bos, unk, pad, eos
											_SCREAMING_SNAKE_CASE					:			List[str]							= []
											_SCREAMING_SNAKE_CASE					:			Any							= []
											_SCREAMING_SNAKE_CASE					:			Dict							= {}
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.add_symbol(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Any							= self.add_symbol(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.add_symbol(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Dict							= self.add_symbol(__snake_case    )
											if extra_special_symbols:
															for s in extra_special_symbols:
																			self.add_symbol(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= len(self.symbols    )






							def __eq__(       self       ,   __snake_case    ):
											return self.indices == other.indices






							def __getitem__(       self       ,   __snake_case    ):
											if idx < len(self.symbols    ):
															return self.symbols[idx]
											return self.unk_word






							def __len__(       self    ):
											return len(self.symbols    )






							def __contains__(       self       ,   __snake_case    ):
											return sym in self.indices






							@classmethod
							def UpperCAmelCase_      (       cls       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= cls()
											d.add_from_file(__snake_case    )
											return d






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case=1       ,   __snake_case=False    ):
											if word in self.indices and not overwrite:
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.indices[word]
															_SCREAMING_SNAKE_CASE					:			Tuple							= self.count[idx] + n
															return idx
											else:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= len(self.symbols    )
															_SCREAMING_SNAKE_CASE					:			List[Any]							= idx
															self.symbols.append(__snake_case    )
															self.count.append(__snake_case    )
															return idx






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											return 0






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											if isinstance(__snake_case       ,   __snake_case    ):
															try:
																			with open(__snake_case       ,   """r"""       ,   encoding="""utf-8"""    ) as fd:
																							self.add_from_file(__snake_case    )
															except FileNotFoundError as fnfe:
																			raise fnfe
															except UnicodeError:
																			raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(__snake_case    )    )
															return

											_SCREAMING_SNAKE_CASE					:			List[Any]							= f.readlines()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self._load_meta(__snake_case    )

											for line in lines[indices_start_line:]:
															try:
																			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[int]							= line.rstrip().rsplit(""" """       ,   1    )
																			if field == "#fairseq:overwrite":
																							_SCREAMING_SNAKE_CASE					:			List[str]							= True
																							_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Tuple							= line.rsplit(""" """       ,   1    )
																			else:
																							_SCREAMING_SNAKE_CASE					:			Tuple							= False
																			_SCREAMING_SNAKE_CASE					:			int							= int(__snake_case    )
																			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= line
																			if word in self and not overwrite:
																							raise RuntimeError(
																							    """Duplicate word found when loading Dictionary: '{}'. """
																							    """Duplicate words can overwrite earlier ones by adding the """
																							    """#fairseq:overwrite flag at the end of the corresponding row """
																							    """in the dictionary file. If using the Camembert model, please """
																							    """download an updated copy of the model file.""".format(__snake_case    )    )
																			self.add_symbol(__snake_case       ,   n=__snake_case       ,   overwrite=__snake_case    )
															except ValueError:
																			raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""    )







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= dict((re.sub(R"""@@$"""				, """"""				, SCREAMING_SNAKE_CASE__	), v) if k.endswith("""@@"""	) else (re.sub(R"""$"""				, """</w>"""				, SCREAMING_SNAKE_CASE__	), v) for k, v in d.items()	)
				_SCREAMING_SNAKE_CASE					:			str							= """<s> <pad> </s> <unk>""".split()
				# restore the special tokens
				for k in keep_keys:
								del da[f"""{k}</w>"""]
								_SCREAMING_SNAKE_CASE					:			List[Any]							= d[k]  # restore
				return da







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if not os.path.exists(SCREAMING_SNAKE_CASE__	):
								raise ValueError(f"""path {biogpt_checkpoint_path} does not exist!"""	)
				os.makedirs(SCREAMING_SNAKE_CASE__				, exist_ok=SCREAMING_SNAKE_CASE__	)
				print(f"""Writing results to {pytorch_dump_folder_path}"""	)

				# handle various types of models

				_SCREAMING_SNAKE_CASE					:			Tuple							= os.path.join(SCREAMING_SNAKE_CASE__				, """checkpoint.pt"""	)
				if not os.path.isfile(SCREAMING_SNAKE_CASE__	):
								raise ValueError(f"""path to the file {checkpoint_file} does not exist!"""	)
				_SCREAMING_SNAKE_CASE					:			str							= torch.load(SCREAMING_SNAKE_CASE__				, map_location="""cpu"""	)

				_SCREAMING_SNAKE_CASE					:			List[Any]							= chkpt["""cfg"""]["""model"""]

				# dicts
				_SCREAMING_SNAKE_CASE					:			Dict							= os.path.join(SCREAMING_SNAKE_CASE__				, """dict.txt"""	)
				if not os.path.isfile(SCREAMING_SNAKE_CASE__	):
								raise ValueError(f"""path to the file {dict_file} does not exist!"""	)
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= Dictionary.load(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= rewrite_dict_keys(src_dict.indices	)
				_SCREAMING_SNAKE_CASE					:			List[Any]							= len(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= os.path.join(SCREAMING_SNAKE_CASE__				, VOCAB_FILES_NAMES["""vocab_file"""]	)
				print(f"""Generating {src_vocab_file} of {src_vocab_size} records"""	)
				with open(SCREAMING_SNAKE_CASE__				, """w"""				, encoding="""utf-8"""	) as f:
								f.write(json.dumps(SCREAMING_SNAKE_CASE__				, ensure_ascii=SCREAMING_SNAKE_CASE__				, indent=SCREAMING_SNAKE_CASE__	)	)

				# merges_file (bpecodes)
				_SCREAMING_SNAKE_CASE					:			int							= os.path.join(SCREAMING_SNAKE_CASE__				, """bpecodes"""	)
				if not os.path.isfile(SCREAMING_SNAKE_CASE__	):
								raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""	)

				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= os.path.join(SCREAMING_SNAKE_CASE__				, VOCAB_FILES_NAMES["""merges_file"""]	)
				shutil.copyfile(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				# model config
				_SCREAMING_SNAKE_CASE					:			int							= os.path.join(SCREAMING_SNAKE_CASE__				, """config.json"""	)

				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= {
				    """activation_dropout""": args["""activation_dropout"""],
				    """architectures""": ["""BioGptForCausalLM"""],
				    """attention_probs_dropout_prob""": args["""attention_dropout"""],
				    """bos_token_id""": 0,
				    """eos_token_id""": 2,
				    """hidden_act""": args["""activation_fn"""],
				    """hidden_dropout_prob""": args["""dropout"""],
				    """hidden_size""": args["""decoder_embed_dim"""],
				    """initializer_range""": 0.0_2,
				    """intermediate_size""": args["""decoder_ffn_embed_dim"""],
				    """layer_norm_eps""": 1E-12,
				    """layerdrop""": args["""decoder_layerdrop"""],
				    """max_position_embeddings""": args["""max_target_positions"""],
				    """model_type""": """biogpt""",
				    """num_attention_heads""": args["""decoder_attention_heads"""],
				    """num_hidden_layers""": args["""decoder_layers"""],
				    """pad_token_id""": 1,
				    """scale_embedding""": not args["""no_scale_embedding"""],
				    """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
				    """vocab_size""": src_vocab_size,
				}

				# good hparam defaults to start with

				print(f"""Generating {biogpt_model_config_file}"""	)
				with open(SCREAMING_SNAKE_CASE__				, """w"""				, encoding="""utf-8"""	) as f:
								f.write(json.dumps(SCREAMING_SNAKE_CASE__				, ensure_ascii=SCREAMING_SNAKE_CASE__				, indent=SCREAMING_SNAKE_CASE__	)	)

				# tokenizer config
				_SCREAMING_SNAKE_CASE					:			Any							= os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			str							= {
				    """bos_token""": """<s>""",
				    """eos_token""": """</s>""",
				    """model_max_length""": 1024,
				    """pad_token""": """<pad>""",
				    """special_tokens_map_file""": None,
				    """tokenizer_class""": """BioGptTokenizer""",
				    """unk_token""": """<unk>""",
				}

				print(f"""Generating {biogpt_tokenizer_config_file}"""	)
				with open(SCREAMING_SNAKE_CASE__				, """w"""				, encoding="""utf-8"""	) as f:
								f.write(json.dumps(SCREAMING_SNAKE_CASE__				, ensure_ascii=SCREAMING_SNAKE_CASE__				, indent=SCREAMING_SNAKE_CASE__	)	)

				# model
				_SCREAMING_SNAKE_CASE					:			Any							= chkpt["""model"""]

				# remove unneeded keys
				_SCREAMING_SNAKE_CASE					:			List[Any]							= [
				    """decoder.version""",
				]
				for k in ignore_keys:
								model_state_dict.pop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			List[Any]							= list(model_state_dict.keys()	)
				for layer_name in layer_names:
								if layer_name.endswith("""output_projection.weight"""	):
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model_state_dict.pop(SCREAMING_SNAKE_CASE__	)
								else:
												_SCREAMING_SNAKE_CASE					:			Dict							= model_state_dict.pop(SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			List[str]							= BioGptConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= BioGptForCausalLM(SCREAMING_SNAKE_CASE__	)

				# check that it loads ok
				model_new.load_state_dict(SCREAMING_SNAKE_CASE__	)

				# save
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				print(f"""Generating {pytorch_weights_dump_path}"""	)
				torch.save(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				print("""Conversion is done!"""	)


if __name__ == "__main__":
				UpperCAmelCase_						:  Union[str, Any]				  =						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--biogpt_checkpoint_path',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
				        ' bpecodes, etc.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				UpperCAmelCase_						:  Any				  =						parser.parse_args()
				convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''


import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							@require_torch
							def UpperCAmelCase_      (       self    ):
											# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
											# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
											# while running an external program

											# python one-liner segments

											# this must be loaded before socket.socket is monkey-patched
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

											_SCREAMING_SNAKE_CASE					:			Dict							= """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
        """

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """
import socket
def offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")
socket.socket = offline_socket
        """

											# Force fetching the files so that we can use the cache
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """hf-internal-testing/tiny-random-bert"""
											BertConfig.from_pretrained(__snake_case    )
											BertModel.from_pretrained(__snake_case    )
											BertTokenizer.from_pretrained(__snake_case    )
											pipeline(task="""fill-mask"""       ,   model=__snake_case    )

											# baseline - just load from_pretrained with normal network
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [sys.executable, """-c""", """\n""".join([load, run, mock]    )]

											# should succeed
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_env()
											# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
											_SCREAMING_SNAKE_CASE					:			int							= """1"""
											_SCREAMING_SNAKE_CASE					:			str							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											# python one-liner segments
											# this must be loaded before socket.socket is monkey-patched
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

											_SCREAMING_SNAKE_CASE					:			Any							= """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
        """

											_SCREAMING_SNAKE_CASE					:			List[Any]							= """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")
socket.socket = offline_socket
        """

											# Force fetching the files so that we can use the cache
											_SCREAMING_SNAKE_CASE					:			Tuple							= """hf-internal-testing/tiny-random-bert"""
											BertConfig.from_pretrained(__snake_case    )
											BertModel.from_pretrained(__snake_case    )
											BertTokenizer.from_pretrained(__snake_case    )
											pipeline(task="""fill-mask"""       ,   model=__snake_case    )

											# baseline - just load from_pretrained with normal network
											_SCREAMING_SNAKE_CASE					:			Tuple							= [sys.executable, """-c""", """\n""".join([load, run, mock]    )]

											# should succeed
											_SCREAMING_SNAKE_CASE					:			str							= self.get_env()
											_SCREAMING_SNAKE_CASE					:			Tuple							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
											# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
											# while running an external program

											# python one-liner segments

											# this must be loaded before socket.socket is monkey-patched
											_SCREAMING_SNAKE_CASE					:			Tuple							= """
from transformers import BertConfig, BertModel, BertTokenizer
        """

											_SCREAMING_SNAKE_CASE					:			str							= """
mname = \"hf-internal-testing/tiny-random-bert-sharded\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print(\"success\")
        """

											_SCREAMING_SNAKE_CASE					:			List[Any]							= """
import socket
def offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")
socket.socket = offline_socket
        """

											# baseline - just load from_pretrained with normal network
											_SCREAMING_SNAKE_CASE					:			List[str]							= [sys.executable, """-c""", """\n""".join([load, run]    )]

											# should succeed
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_env()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )

											# next emulate no network
											_SCREAMING_SNAKE_CASE					:			List[str]							= [sys.executable, """-c""", """\n""".join([load, mock, run]    )]

											# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
											# env["TRANSFORMERS_OFFLINE"] = "0"
											# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
											# self.assertEqual(result.returncode, 1, result.stderr)

											# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
											_SCREAMING_SNAKE_CASE					:			Dict							= """1"""
											_SCREAMING_SNAKE_CASE					:			int							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """
from transformers import pipeline
        """
											_SCREAMING_SNAKE_CASE					:			int							= """
mname = \"hf-internal-testing/tiny-random-bert\"
pipe = pipeline(model=mname)
        """

											_SCREAMING_SNAKE_CASE					:			Any							= """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")
socket.socket = offline_socket
        """
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_env()
											_SCREAMING_SNAKE_CASE					:			Any							= """1"""
											_SCREAMING_SNAKE_CASE					:			str							= [sys.executable, """-c""", """\n""".join([load, mock, run]    )]
											_SCREAMING_SNAKE_CASE					:			List[Any]							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   1       ,   result.stderr    )
											self.assertIn(
											    """You cannot infer task automatically within `pipeline` when using offline mode"""       ,   result.stderr.decode().replace("""\n"""       ,   """"""    )       ,   )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """
from transformers import AutoModel
        """
											_SCREAMING_SNAKE_CASE					:			Tuple							= """
mname = \"hf-internal-testing/test_dynamic_model\"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print(\"success\")
        """

											# baseline - just load from_pretrained with normal network
											_SCREAMING_SNAKE_CASE					:			List[str]							= [sys.executable, """-c""", """\n""".join([load, run]    )]

											# should succeed
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_env()
											_SCREAMING_SNAKE_CASE					:			List[str]							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )

											# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """1"""
											_SCREAMING_SNAKE_CASE					:			Any							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )




